Computer architecture for emulating a correlithm object logic gate using a context input

ABSTRACT

A device configured to emulate a correlithm object logic function gate comprises a memory and a logic engine. The memory stores a logical operator truth table that includes a plurality of input logical values, a plurality of output logical values, and a plurality of logical operators. These logical values and the logical operators are represented by correlithm objects. The logic engine receives at least one input and a context input correlithm object representing one of the plurality of logical operators. The logic engine determines a portion of the truth table to apply based at least in part upon the logical operator represented by the context input correlithm object. The logic engine further determine an output of the logic function gate based at least in part upon the determined portion of the truth table to apply and the input.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to computerarchitectures for emulating a correlithm object logic gate using acontext input.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asface recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin an set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system is able to use correlithm objects in facialrecognition applications to quickly determine whether a captured imageof a person matches any previously stored images without relying onconventional signal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, correlithm object processing system uses a combination of asensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice is able to determine how similar the data samples are to eachother even when the data samples are not exact matches. In addition, thedevice is able to quantify how similar data samples are to one another.The ability to determine how similar data samples are to each others isunique and distinct from conventional computers that can only performbinary comparisons to identify exact matches.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device is able to determine a degree of similarity thatquantifies how similar different data samples are to one another.

In conventional electronic and computer systems, digital circuitsrepresent a single state using two logic levels. These levels arereferred to, for example, as a logic 1 or a logic 0, HIGH or LOW, Trueor False, or ON or OFF. Most logic systems use positive logic, in whichcase a logic “0” is represented by zero volts and a logic “1” isrepresented by a higher voltage, such as +5 volts. The two discretevoltage levels representing the digital values of “1's” (one's) and“0's” (zero's) are commonly called: BInary digiTS, and in digital andcomputational circuits and applications they are normally referred to asbinary BITS.

Conventional electronic and computer circuits exhibit some small levelof noise. Thus, a noise margin is built around the zero voltrepresentation of a logic “0” and around a +5 volt representation oflogic “1” to allow the system to accurately represent a logic 0 or 1even if the voltage levels are not exactly zero or +5 volts. However,voltage surges associated with electromagnetic pulses (EMPs) or othersignificant forms of noise can alter the representation of these binaryBITS in a conventional digital computer system. For example, a voltagesurge can exceed the noise margins built around logic “0” and “1” suchthat a BIT value of “0” may be mispresented as a value of “1,” and a BITvalue of “1” may be misrepresented as a value of “0.” Thus, voltagesurge can cause conventional electronic and computer circuits tomalfunction.

The present disclosure describes representing logical “0's” and “1's”(or High/Low; True/False; On/Off) using correlithm objects which exhibitimmunity to these significant noise impulses. Although the examplesbelow are described in terms of logical “0's” and logical “1's,” one ofskill in the art will understand that any binary values (e.g., High/Low;True/False; On/Off; etc.) can be used to represent these logical values.As a result of using correlithm objects to represent these logicalvalues, any electrical or computer system built using correlithm objectsto represent binary values is more robust and hardened against voltagesurges and other forms of significant noise, such as EMP impulses. Forexample, when a logical “0” is represented by a correlithm object inn-dimensional space (e.g., a correlithm object in 64-dimensional spacecomprising a 64-bit value to represent a logical “0”), an EMP noiseimpulse may affect a few bits (e.g., up to five bits) of the 64-bitcorrelithm object. However, the remaining bits (e.g., up to sixty bits)of the 64-bit correlithm object would remain intact and collectivelywould still represent a logical “0.” Similarly, when a logical “1” isrepresented by a correlithm object in n-dimensional space (e.g., acorrelithm object in 64-dimensional space comprising a 64-bit value torepresent a logical “1”), an EMP noise impulse may affect a few bits(e.g., up to five bits) of the 64 bit correlithm object. However, theremaining bits (e.g., up to sixty bits) of the 64-bit correlithm objectwould remain intact and collectively would still represent a logical“1.” In other words, because the logical “0” or logical “1” isrepresented in n-dimensional space using a correlithm object, even ifthe values of some of the bits of the correlithm object were affected bysignificant noise, the remaining bits would adequately represent theoriginal logical value.

Therefore, correlithm objects can be used to emulate logical values andlogical functions with a robustness and immunity to noise that is notavailable in conventional computer systems. In at least this way, theuse of correlithm objects to emulate logical values and logicalfunctions improves the operation of a computer itself.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIG. 6 illustrates one embodiment of a correlithm object binary logicfunction gate;

FIG. 7 illustrates one embodiment of a flowchart implementing a processperformed by binary logic function gate;

FIG. 8 illustrates one embodiment of a correlithm object unary logicfunction gate;

FIG. 9 illustrates one embodiment of a flowchart implementing a processperformed by unary logic function gate; and

FIG. 10 illustrates one embodiment of a correlithm object binaryflip-flop device.

DETAILED DESCRIPTION

FIGS. 1-5 describe various embodiments of how a correlithm objectprocessing system may be implemented or emulated in hardware, such as aspecial purpose computer. FIGS. 6-10 describe various embodiments ofusing correlithm objects to emulate logic gates.

FIG. 1 is a schematic view of an embodiment of a user device 100implementing correlithm objects 104 in an n-dimensional space 102.Examples of user devices 100 include, but are not limited to, desktopcomputers, mobile phones, tablet computers, laptop computers, or otherspecial purpose computer platform. The user device 100 is configured toimplement or emulate a correlithm object processing system that usescategorical numbers to represent data samples as correlithm objects 104in a high-dimensional space 102, for example a high-dimensional binarycube. Additional information about the correlithm object processingsystem is described in FIG. 3. Additional information about configuringthe user device 100 to implement or emulate a correlithm objectprocessing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each others is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engine are limitedto finding related data samples that have text that exactly matchesother data samples. These search engines only provide a binary resultthat identifies whether or not an exact match was found based on thesearch token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search time when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value of ‘n’represents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance or anyother suitable technique.

As an example using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds to the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. In other words, the Hamming distance between thefirst correlithm object 104 and the second correlithm object 104 can becomputed as follows:

$\begin{matrix}1001011011 \\1000011011 \\\text{-----------------} \\0001000000\end{matrix}$

In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object. As another example, a third correlithm object 104 isrepresented by a third 10-bit string (0110100100). In this example, theHamming distance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

$\begin{matrix}1001011011 \\0110100100 \\\text{-----------------} \\1111111111\end{matrix}$

The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. In the previous example, a Hamming distance equalto one indicates that the first correlithm object 104 and the secondcorrelithm object 104 are close to each other in the n-dimensional space102, which means they are similar to each other. In the second example,a Hamming distance equal to ten indicates that the first correlithmobject 104 and the third correlithm object 104 are further from eachother in the n-dimensional space 102 and are less similar to each otherthan the first correlithm object 104 and the second correlithm object104. In other words, the similarity between a pair of correlithm objectscan be readily determined based on the distance in n-dimensional spacebetween the pair correlithm objects.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples between realworld values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples having realworld values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat in order to perform any kind of operation on the data samples. Insome instances, some types of data samples cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number dimensions or a differentnumber of dimensions. For example, the first n-dimensional space 102Aand the second n-dimensional space 102B may both be three dimensionalspaces. As another example, the first n-dimensional space 102A may be athree dimensional space and the second n-dimensional space 102B may be anine dimensional space. Correlithm objects 104 in the firstn-dimensional space 102A and second n-dimensional space 102B are mappedto each other. In other words, a correlithm object 104A in the firstn-dimensional space 102A may reference or be linked with a particularcorrelithm object 104B in the second n-dimensional space 102B. Thecorrelithm objects 104 may also be linked with and referenced with othercorrelithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 194 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100compare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on the realworld value 320, and to output the correlithm object 104. For example,the sensor 302 may receive an image 301 of a person and output acorrelithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real world values are any type ofsignal, value, or representation of data samples. Examples of real worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real world value 320 and acorrelithm object 104 is a n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back to realworld values or data samples. Actors 306 enable the user device 100 toconvert from correlithm objects 104 into any suitable type of real worldvalue. Actors 306 are configured to receive a correlithm object 104(e.g. an output correlithm object 104), to determine a real world outputvalue 326 based on the received correlithm object 104, and to output thereal world output value 326. The real world output value 326 may be adifferent data type or representation of the original data sample. As anexample, the real world input value 320 may be an image 301 of a personand the resulting real world output value 326 may be text 327 and/or anaudio signal identifying the person. In one embodiment, actors 306 areconfigured to use actor tables 310 that link a plurality of correlithmobjects 104 in an n-dimensional space 102 with a plurality of real worldvalues. As an example, an actor table 310 may be configured with a firstcolumn 316 that lists correlithm objects 104 as output correlithmobjects and a second column 318 that lists real world values. In otherexamples, actor tables 310 may be configured in any other suitablemanner or may be implemented using any other suitable data structure. Insome embodiments, one or more mapping functions may be employed totranslate between a correlithm object 104 in an n-dimensional space anda real world output value 326. Additional information for implementingor emulating an actor 306 in hardware is described in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) are to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real world value entry in the sensor table 308that matches the input signal. For example, the real world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds a realworld value entry that closest matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real world outputvalue in the actor table 310 linked with the output correlithm object104. The real world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, the realworld output value may be text that indicates the name of the person inthe image or some other identifier associated with the person in theimage. As another example, the real world output value may be an audiosignal or sample of the name of the person in the image. In otherexamples, the real world output value may be any other suitable realworld signal or value that corresponds with the original input signal.The real world output value may be in any suitable data type or format.

At step 424, the actor 306 outputs the identified real world outputvalue. In one embodiment, the actor 306 may output the real world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real world output valueto a memory or database. In one embodiment, the real world output valueis sent to another sensor 302. For example, the real world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment a computer architecture500 for emulating a correlithm object processing system 300 in a userdevice 100. The computer architecture 500 comprises a processor 502, amemory 504, a network interface 506, and an input-output (I/O) interface508. The computer architecture 500 may be configured as shown or in anyother suitable configuration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement sensor engines 510, node engines 512,actor engines 514 and logic engines 515 that implement logic gates 600,800, and 1000. In an embodiment, the sensor engines 510, the nodeengines 512, and the actor engines 514 are implemented using logicunits, FPGAs, ASICs, DSPs, or any other suitable hardware. The sensorengines 510, the node engines 512, and the actor engines 514 are eachconfigured to implement a specific set of rules or process that providesan improved technological result.

In one embodiment, the sensor engine 510 is configured to receive a realworld value 320 as an input, to determine a correlithm object 104 basedon the real world value 320, and to output the correlithm object 104.Examples of the sensor engine 510 in operation are described in FIG. 4.

In one embodiment, the node engine 512 is configured to receive acorrelithm object 104 (e.g. an input correlithm object 104), todetermine another correlithm object 104 based on the received correlithmobject 104, and to output the identified correlithm object 104 (e.g. anoutput correlithm object 104). The node engine 512 is also configured tocompute distances between pairs of correlithm objects 104. Examples ofthe node engine 512 in operation are described in FIG. 4.

In one embodiment, the actor engine 514 is configured to receive acorrelithm object 104 (e.g. an output correlithm object 104), todetermine a real world output value 326 based on the received correlithmobject 104, and to output the real world output value 326. Examples ofthe actor engine 514 in operation are described in FIG. 4.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, actor instructions520, sensor tables 308, node tables 200, actor tables 310, truth tables612, 812, and 1008, and/or any other data or instructions. The sensorinstructions 516, the node instructions 518, and the actor instructions520 comprise any suitable set of instructions, logic, rules, or codeoperable to execute the sensor engine 510, node engine 512, and theactor engine 514, respectively.

The sensor tables 308, the node tables 200, and the actor tables 310 maybe configured similar to the sensor tables 308, the node tables 200, andthe actor tables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIG. 6 illustrates one embodiment of a binary correlithm object logicfunction gate 600 that receives a first input correlithm object 602 anda second input correlithm object 604, implements a particular logicaloperator 606 according to a context input correlithm object 608 (in oneembodiment), and outputs an output correlithm object 610. Binarycorrelithm object logic function gate 600 implements up to sixteendifferent logical operators 606 upon binary values represented by inputscorrelithm objects 602 and 604. The logical operators 606 arerepresented by correlithm objects in a truth table 612, an example ofwhich is illustrated in FIG. 6. Depending on the binary values that areinput into gate 600 and the portion of the truth table 612 associatedwith the logical operator 606 that is implemented, gate 600 outputs anoutput correlithm object 610 representing a logical “0” or “1”.

In one embodiment, the system will assign the same correlithm object forinput 602 to represent a logical “0” as the correlithm object torepresent a logical “0” in row 614 of the truth table 812. Further, thesystem will assign the same correlithm object for input 602 to representa logical “1” as the correlithm object to represent a logical “1” in row614 of the truth table 812. Similarly, the system will assign the samecorrelithm object for input 604 to represent a logical “0” as thecorrelithm object to represent a logical “0” in row 616 of the truthtable 812. Further, the system will assign the same correlithm objectfor input 604 to represent a logical “1” as the correlithm object torepresent a logical “1” in row 616 of the truth table 812.

The operation of logic function gate 600 is described below inconjunction with a particular example. In this example, assume thatbinary correlithm object logic function gate 600 implements only thelogical “Not AND (NAND)” function or, alternatively in one embodiment,assume that binary correlithm object logic function gate 600 implementsa number of logical operators 606 and that context input correlithmobject 608 represents a value that selects the “Not AND (NAND)”function. In particular, each of the logical operators 606 arerepresented by a different, corresponding correlithm object.

The context input 608 is also represented by a correlithm object that isset to match the correlithm object of the logical operator 606 to beapplied in a particular operation of logic function gate 600. Forexample, if the NAND function is to be applied by logic function gate600, then the context input 608 is represented by a correlithm objectthat is set to the same n-bit (e.g., 64-bit) word of binary values asthe correlithm object that represents the NAND function logical operator606. When the correlithm object of the context input 608 is received,the logic function gate 600 determines the distance in n-dimensionalspace between the correlithm object of the context input 608 and each ofthe correlithm objects representing the logical operators 606. The logicfunction gate 600 selects for application whichever correlithm objectrepresenting a logical operator 606 is closest in n-dimensional space tothe correlithm object of the context input 608. For example, in oneembodiment, the logic function gate 600 determines the Hamming distancebetween the correlithm object of the context input 608 and each of thecorrelithm objects representing the logical operators 606, and selectsthe logical operator 606 based on which one has the smallest Hammingdistance between its correlithm object and the correlithm object of thecontext input 608. Thus, if the correlithm object of the context input608 is the closest in n-dimensional space to the correlithm objectrepresenting the NAND gate (e.g., has the smallest Hamming distance),then the portion of the truth table 612 that corresponds to the NANDfunction (e.g., ordered logical values c1, c1, c1, c0) is used by thelogic function gate 600 in determining (at least in part) the output610.

Referring back to the example, further assume that first inputcorrelithm object 602 is a 64-bit correlithm object that represents alogical “0”. For example, input correlithm object 602 may comprise aunique 64-bit word of “1's” and “0's” that is assigned to represent alogical “0”. Further assume that second input correlithm object 604 is a64-bit correlithm object that represents a logical “0”. For example,input correlithm object 604 may comprise a different unique 64-bit wordof “1's” and “0's” that is also assigned to represent a logical “0”.

Binary logic function gate 600 determines the Hamming distance (orotherwise determines the distance in n-dimensional space) between inputcorrelithm object 602 and the correlithm objects in row 614 of the truthtable 612. Even if noise modifies some of the bits of the n-bit inputcorrelithm object 602 and/or some of the bits of the n-bit correlithmobjects representing the logical “0” and “1” values in row 614 of thetruth table 612, the Hamming distance between the input correlithmobject 602 representing a logical “0” and the correlithm objectrepresenting logical “0's” in row 614 (shown in the cells where row 614intersects columns 620 and 622 of the truth table 612) will be smallerthan the Hamming distance between the input correlithm object 602representing a logical “0” and the correlithm object representinglogical “1's” in row 614 (shown in the cells where row 614 intersectscolumns 624 and 626 of the truth table 612). The smaller Hammingdistance calculation means that the input correlithm object 602 iscloser in n-dimensional space to the correlithm object representinglogical “0's” in row 614 than to the correlithm object representinglogical “1's” in row 614. Thus, columns 620 and 622 are selected asmatches for input correlithm object 602 based upon these Hammingdistance calculations.

Logic function gate 600 also determines the Hamming distance (orotherwise determines the distance in n-dimensional space) between inputcorrelithm object 604 and the correlithm objects in row 616 of the truthtable 612. Even if noise modifies some of the bits of the n-bit inputcorrelithm object 604 and some of the bits of the n-bit correlithmobjects representing the logical “0” and “1” values in row 616, theHamming distance between the correlithm object input 604 representing alogical “0” and the correlithm object representing logical “0's” in row616 (shown in the cells where row 616 intersects columns 620 and 624 ofthe truth table 612) will be smaller than the Hamming distance betweenthe input correlithm object 604 representing a logical “0” and thecorrelithm object representing logical “1's” in row 616 (shown in thecells where row 616 intersects columns 622 and 626 of the truth table612). The smaller Hamming distance calculation means that the inputcorrelithm object 604 is closer in n-dimensional space to the correlithmobject representing logical “0's” in row 616 than to the correlithmobject representing logical “1's” in row 616. Thus, columns 620 and 624are selected as matches for correlithm object input 604 based upon theseHamming distance calculations.

Because the values in columns 620 and 622 were selected as matches forinput correlithm object 602 and the values in columns 620 and 624 wereselected as matches for input correlithm object 604, the only columnthat was selected as a match for both input correlithm objects 602 and604 is column 620. Column 620 includes a logical “0” in row 614 whichmaps to the logical “0” represented by input 602, and column 620 furtherincludes a logical “0” in row 616 which maps to the logical “0”represented by input 604. According to this set of inputs 602 and 604and upon implementing the NAND function to the values in column 620, theresulting binary value is logical “1.” Output correlithm object 610 maycomprise a unique n-bit (e.g., 64-bit) word of 1's and 0's that isassigned to represent this resulting logical “1.” In this way, the logicfunction gate 600 can implement the logical operators 606 associatedwith truth table 612 using correlithm objects rather than traditionalvoltage values to represent logical “0's” and “1's.” Although the aboveexample describes a NAND function, one of skill in the art willrecognize that any of the logical operators 606 illustrated in FIG. 6can be implemented either by building a logic function gate 600dedicated to that logical operator 606, or by using a context inputcorrelithm object 608 to identify a particular logical operator 606 in amulti-faceted logic function gate 600.

Implementing one or more logical operators 606 using correlithm objects,as described above, improves the operation of any electrical or computersystem that uses such a logic gate 600. In particular, whereas a zerovoltage that may represent a logical “0” in a conventional logic gatefor a traditional computer system may be altered by a significant noiseevent (e.g., voltage surge caused by EMP) such that the logical valuemisrepresents the logical “0” as a logical “1” (or vice versa), then-bit word of 1's and 0's that forms the input correlithm objects 602and 604 would remain largely unaffected by noise. In particular, perhapsa few of the bits of the n-bit input correlithm objects 602 or 604 wouldbe affected by the noise. Or, perhaps a few of the bits of the n-bitcorrelithm object values in the truth table 612 would be affected by thenoise. However, the remaining bits of the n-bit input correlithm objects602 or 604 (or correlithm objects representing values in the truth table612) would remain close enough to the core of the original inputcorrelithm object 602 or 604 (or correlithm objects representing valuesin the truth table 612) to accurately represent the original logicalvalue. Thus, upon calculating the Hamming distance between the inputcorrelithm object 602 (or 604) with the correlithm objects representinglogical “0's” or “1's” in row 614 (or row 616), the system wouldassociate the input correlithm object 602 (or 604) with the correctvalues in the truth table 612 with a greater degree of likelihood thanin conventional computer systems that experience a significant noiseevent. Similarly, a significant noise event would not significantlyaffect the output correlithm object 610 either. In particular, perhaps afew of the bits of the n-bit output correlithm object 610 would beaffected by the noise. However, the remaining bits of the n-bit outputcorrelithm object 610 would remain close enough to the core of theoriginal output correlithm object 610 to accurately represent theoriginal logical value of “1.” In this way, the implementation of acorrelithm object logic function gate 600 improves the operation of theunderlying electronic circuit or computer in comparison to conventionalapproaches of implementing logic gates.

In one embodiment, a different n-bit correlithm object is used torepresent a logical “0” or logical “1” in input 602 than is used forinput 604. Similarly, a different n-bit correlithm object is used torepresent a logical “0” or logical “1” in output 610 than is used ineither inputs 602 or 604. However, the same correlithm object is used torepresent the logical “0's” in columns 620 and 622 of row 614; and thesame correlithm object is used to represent the logical “1's” in columns624 and 626 of row 614. Similarly, the same correlithm object is used torepresent the logical “0's” in columns 620 and 624 of row 616; and thesame correlithm object is used to represent the logical “1's” in columns622 and 626 of row 616.

FIG. 7 illustrates one embodiment of a flowchart 700 implementing aprocess performed by binary correlithm object logic function gate 600which stores in memory a logical operator truth table 612 at step 702.Logic function gate 600 receives a first input correlithm object 602 atstep 704 and a second input correlithm object 604 and step 706. Logicfunction gate 600 further receives a context input correlithm object 608at step 708. At step 710, logic function gate 600 determines ann-dimensional distance (e.g., Hamming distance) between first inputcorrelithm object 602 and the correlithm object representing logical“0's” in the first group of input values in truth table 612, such as inthe cells where row 614 and columns 620 and 622 intersect in truth table612 illustrated in FIG. 6. At step 712, logic function gate 600determines an n-dimensional distance (e.g., Hamming distance) betweenfirst input correlithm object 602 and the correlithm object representinglogical “1's” in the first group of input values in truth table 612,such as in the cells where row 614 and columns 624 and 626 intersect intruth table 612 illustrated in FIG. 6.

If the n-dimensional distance between first input correlithm object 602and the correlithm object representing logical “0's” is closer than then-dimensional distance between first input correlithm object 602 and thecorrelithm object representing logical “1's,” (e.g., smaller Hammingdistance) as determined at step 714, then execution proceeds to step716. Otherwise, execution proceeds to step 718. At step 716, logicfunction gate 600 selects the logical “0” values, such as the cellswhere row 614 intersects columns 620 and 622 of truth table 612. At step718, logic function gate 600 selects the logical “1” values, such as thecells where row 614 intersects columns 624 and 626 of truth table 612.

Execution proceeds to step 720 where logic function gate 600 determinesan n-dimensional distance (e.g., Hamming distance) between second inputcorrelithm object 604 and the correlithm object representing logical“0's” in the second group of input values in truth table 612, such as inthe cells where row 616 and columns 620 and 624 intersect in truth table612 illustrated in FIG. 6. At step 722, logic function gate 600determines an n-dimensional distance (e.g., Hamming distance) betweensecond input correlithm object 604 and the correlithm objectrepresenting logical “1's” in the second group of input values in truthtable 612, such as in the cells where row 616 and columns 622 and 626intersect in truth table 612 illustrated in FIG. 6.

If the n-dimensional distance between second input correlithm object 604and the correlithm object representing logical “0's” is closer than then-dimensional distance between second input correlithm object 604 andthe correlithm object representing logical “1's,” (e.g., smaller Hammingdistance) as determined at step 724, then execution proceeds to step726. Otherwise, execution proceeds to step 728. At step 726, logicfunction gate 600 selects the logical “0” values, such as the cellswhere row 616 intersects columns 620 and 624 of truth table 612. At step728, logic function gate 600 selects the logical “1” values, such as thecells where row 616 intersects columns 622 and 626 of truth table 612.

Execution proceeds to step 730 where logic function gate 600 determinesthe n-dimensional distance (e.g., Hamming distance) between the contextinput correlithm object 608 and each correlithm object representinglogical operators 606 in truth table 612. At step 732, logic functiongate 600 selects the logical operator 606 with the closest n-dimensionaldistance (e.g., smallest Hamming distance) to the context inputcorrelithm object 608. Execution proceeds to step 734 where logicfunction gate 600 determines output correlithm object 610 based on thelogical “0's” or “1's” selected at steps 716 or 718, the logical “0's”or “1's” selected at steps 726 or 728, and the logical operator 606selected at step 732.

For example, if logical “0's” are selected at steps 716 and 726, thenlogic function gate 600 determines an appropriate output correlithmobject 610 from column 620 of truth table 612 based on the logicaloperator 606 that is selected at step 732. If logical “1's” are selectedat steps 718 and 728, then logic function gate 600 determines anappropriate output correlithm object 610 from column 626 of truth table612 based on the logical operator 606 that is selected at step 732. Iflogical “0” is selected at step 716 and logical “1” is selected at step728, then logic function gate 600 determines an appropriate outputcorrelithm object 610 from column 622 of truth table 612 based on thelogical operator 606 that is selected at step 732. If logical “1” isselected at step 718 and logical “0” is selected at step 726, then logicfunction gate 600 determines an appropriate output correlithm object 610from column 624 of truth table 612 based on the logical operator 606that is selected at step 732. Execution terminates at step 736.

FIG. 8 illustrates one embodiment of a unary correlithm object logicfunction gate 800 that receives an input correlithm object 802,implements a particular logical operator 806 according to a contextinput correlithm object 808 (in one embodiment), and outputs an outputcorrelithm object 810. Unary correlithm object logic function gate 800implements up to four different logical operators 806 upon binary valuesrepresented by input correlithm object 802. The logical operators 806are represented by correlithm objects in a truth table 812, an exampleof which is illustrated in FIG. 8. Depending on the binary values thatare input into gate 800 and the portion of the truth table 812associated with the logical operator 806 that is implemented, gate 800outputs an output correlithm object 810 representing a logical “0” or“1”. In one embodiment, the system will assign the same correlithmobject for input 802 to represent a logical “0” as the correlithm objectto represent a logical “0” in row 814 of the truth table 812. Further,the system will assign the same correlithm object for input 802 torepresent a logical “1” as the correlithm object to represent a logical“1” in row 814 of the truth table 812.

The operation of logic function gate 800 is described below inconjunction with a particular example. In this example, assume thatunary correlithm object logic function gate 800 implements only thelogical “Equivalent” function or, alternatively in one embodiment,assume that unary correlithm object logic function gate 800 implements anumber of logical operators 806 and that correlithm object context input808 represents a value that selects the “Equivalent” logical operator.

In particular, each of the logical operators 806 are represented by adifferent, corresponding correlithm object. The context input 808 isalso represented by a correlithm object that is set to match thecorrelithm object of the logical operator 806 to be applied in aparticular operation of logic function gate 800. For example, if theEquivalent function is to be applied by logic function gate 800, thenthe context input 808 is represented by a correlithm object that is setto the same n-bit (e.g., 64-bit) word of binary values as the correlithmobject that represents the Equivalent function logical operator 806.When the correlithm object of the context input 808 is received, thelogic function gate 800 determines the distance in n-dimensional spacebetween the correlithm object of the context input 808 and each of thecorrelithm objects representing the logical operators 806. The logicfunction gate 800 selects for application whichever correlithm objectrepresenting a logical operator 806 is closest in n-dimensional space tothe correlithm object of the context input 808. For example, in oneembodiment, the logic function gate 800 determines the Hamming distancebetween the correlithm object of the context input 808 and each of thecorrelithm objects representing the logical operators 806, and selectsthe logical operator 806 based on which one has the smallest Hammingdistance between its correlithm object and the correlithm object of thecontext input 808. Thus, if the correlithm object of the context input808 is the closest in n-dimensional space to the correlithm objectrepresenting the NAND gate (e.g., has the smallest Hamming distance),then the portion of the truth table 812 that corresponds to the NANDfunction (e.g., ordered logical values c0, c1) is used by the logicfunction gate 800 in determining (at least in part) the output 810.

Referring back to the example, further assume that input correlithmobject 802 is an n-bit (e.g., 64-bit) correlithm object that representsa logical “0”. For example, input correlithm object 802 may comprise aunique n-bit (e.g., 64-bit) word of 1's and 0's that is assigned torepresent a logical “0”. Logic function gate 800 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between correlithm object input 802 and the correlithm objects in row814 of the truth table 812. Even if noise modifies some of the bits ofthe n-bit input correlithm object 802 and/or some of the bits of then-bit correlithm objects representing the logical “0” and “1” values inrow 814 of the truth table 812, the Hamming distance between the inputcorrelithm object 802 representing a logical “0” and the correlithmobject representing a logical “0” in row 814 (shown in the cell whererow 614 intersects with column 816 of the truth table 812) will besmaller than the Hamming distance between the correlithm object input802 representing a logical “0” and the correlithm object representing alogical “1” in row 614 (shown in the cell where row 614 intersectscolumn 818 of the truth table 812). The smaller Hamming distancecalculation means that the correlithm object for input 802 is closer inn-dimensional space to the correlithm object representing a logical “0”in row 814 than to the correlithm object representing a logical “1” inrow 814. Thus, column 816 is selected as a match for correlithm objectinput 802 based upon these Hamming distance calculations.

According to this input 802 and upon implementing the Equivalentfunction, the resulting binary value is “0”. Output correlithm object810 may comprise a unique n-bit (e.g., 64-bit) word of 1's and 0's thatis assigned to represent this logical “0.” Conversely, if inputcorrelithm object 802 had represented a logical “1,” then column 818would have been selected based upon the Hamming distance calculations,and upon implementing the Equivalent function, the resulting binaryvalue would be a logical “1”. Output correlithm object 810 may comprisea unique n-bit (e.g., 64-bit) word of 1's and 0's that is assigned torepresent the logical “1” in this instance. In this way, the logicfunction gate 800 can implement the logical operators associated withtruth table 812 using correlithm objects rather than traditional voltagevalues to represent logical “0's” and “1's.” Although the above exampledescribes an Equivalent function, one of skill in the art will recognizethat any of the logical operators 806 illustrated in FIG. 8 can beimplemented either by building a logic function gate 800 dedicated tothat logical operator 806, or by using a context input 808 to identify aparticular logical operator 806 in a multi-faceted logic function gate800.

Implementing one or more logical operators 806 using correlithm objects,as described above, improves the operation of any electrical or computersystem that uses such a logic gate 800. In particular, whereas a zerovoltage that may represent a logical “0” in a conventional logic gatefor a traditional computer system may be altered by a significant noiseevent (e.g., voltage surge caused by EMP) such that the logical valuemisrepresents the logical “0” as a logical “1” (or vice versa), then-bit word of 1's and 0's that forms the input correlithm object 802would remain largely unaffected by the noise. In particular, perhaps afew of the bits of the n-bit input correlithm object 802 would beaffected by noise. Or, perhaps a few of the bits of the n-bit correlithmobject values in the truth table 812 would be affected by the noise.However, the remaining bits of the n-bit input correlithm object 802 (orcorrelithm objects representing values in the truth table 812) wouldremain close enough to the core of the original input correlithm object802 (or correlithm objects representing values in the truth table 812)to accurately represent the original logical value. Thus, uponcalculating the Hamming distance between the input correlithm object 802with the correlithm objects representing logical “0's” or “1's” in row814, the system would associate the input correlithm object 802 with thecorrect values in the truth table 812 with a greater degree oflikelihood than in conventional computer systems that experience asignificant noise event. Similarly, a significant noise event would notsignificantly affect the output correlithm object 810 either. Inparticular, perhaps a few of the bits of the n-bit output correlithmobject 810 would be affected by noise. However, the remaining bits ofthe n-bit output correlithm object 810 would remain close enough to thecore of the output original correlithm object 810 to accuratelyrepresent the original logical value of “1.” In this way, theimplementation of a correlithm object logic function gate 800 improvesthe operation of the underlying electronic circuit or computer incomparison to conventional approaches of implementing logic gates. Inone embodiment, a different n-bit correlithm object is used to representa logical “0” or logical “1” in output 810 than is used in either input802.

FIG. 9 illustrates one embodiment of a flowchart 900 implementing aprocess performed by unary logic function gate 800 which stores inmemory a logical operator truth table 812 at step 902. Logic functiongate 800 receives an input correlithm object 802 at step 804 and acontext input correlithm object 808 at step 906. At step 908, logicfunction gate 800 determines an n-dimensional distance (e.g., Hammingdistance) between input correlithm object 802 and the correlithm objectrepresenting a logical “0” in the input values of truth table 812, suchas in the cell where row 814 and column 816 intersects in truth table812 illustrated in FIG. 8. At step 910, logic function gate 800determines an n-dimensional distance (e.g., Hamming distance) betweeninput correlithm object 802 and the correlithm object representing alogical “1” in the input values of truth table 612, such as in the cellwhere row 814 and column 818 intersects in truth table 812 illustratedin FIG. 6.

If the n-dimensional distance between input correlithm object 802 andthe correlithm object representing a logical “0” is closer than then-dimensional distance between input correlithm object 802 and thecorrelithm object representing a logical “1,” (e.g., smaller Hammingdistance) as determined at step 912, then execution proceeds to step914. Otherwise, execution proceeds to step 916. At step 918, logicfunction gate 800 selects the logical “0” value, such as the cell whererow 814 intersects column 816 of truth table 812. At step 916, logicfunction gate 800 selects the logical “1” value, such as the cell whererow 814 intersects column 818 of truth table 812.

Execution proceeds to step 918 where logic function gate 800 determinesthe n-dimensional distance (e.g., Hamming distance) between the contextinput correlithm object 808 and each correlithm object representinglogical operators 806 in truth table 812. At step 920, logic functiongate 800 selects the logical operator 806 with the closest n-dimensionaldistance (e.g., smallest Hamming distance) to the context inputcorrelithm object 808. Execution proceeds to step 922 where logicfunction gate 800 determines output correlithm object 810 based on thelogical “0” or “1” selected at steps 914 or 916, and the logicaloperator 806 selected at step 920.

For example, if a logical “0” is selected at step 914, then logicfunction gate 800 determines an appropriate output correlithm object 810from column 816 of truth table 812 based on the logical operator 806that is selected at step 920. If a logical “1” is selected at step 916,then logic function gate 800 determines an appropriate output correlithmobject 810 from column 818 of truth table 812 based on the logicaloperator 806 that is selected at step 920. Execution terminates at step924.

FIG. 10 illustrates one embodiment of a binary correlithm objectflip-flop device 1000 that receives a state input correlithm object 1002and a set/reset correlithm object 1004, and outputs an output correlithmobject 1006, which is returned as feedback to device 1000 as state inputcorrelithm object 1002. Device 1000 implements a truth table 1008 thathas two stable states and can be used to store state information fordevice 1000. Depending on the current state of device 1000, the binaryvalues that are input into device 1000, and their relationship inn-dimensional space to the logical values stored in truth table 1008,device 1000 outputs output correlithm object 1006 representing a logicalstate (e.g., logical “0” or logical “1”).

In one embodiment, the system will assign the same correlithm object forinput 1002 to represent a logical “0” as the correlithm object torepresent a logical “0” in row 1010 of the truth table 1008. Further,the system will assign the same correlithm object for input 1002 torepresent a logical “1” as the correlithm object to represent a logical“1” in row 1010 of the truth table 1008. Similarly, the system willassign the same correlithm object for input 1004 to represent a logical“0” as the correlithm object to represent a logical “0” in row 1012 ofthe truth table 1008. Further, the system will assign the samecorrelithm object for input 1004 to represent a logical “1” as thecorrelithm object to represent a logical “1” in row 1012 of the truthtable 1008.

Each of the correlithm objects described in conjunction with device 1000are n-bit digital words comprising binary values. However, the set/resetinput correlithm object 1004 and the corresponding correlithm objects inrow 1012 of the truth table 1008 are larger n-bit digital words than thestate input correlithm object 1002 and the corresponding correlithmobjects in row 1010 of truth table 1008. For example, in one embodiment,the set/reset input correlithm object 1004 and the correspondingcorrelithm objects in row 1012 of the truth table 1008 are 128-bitdigital words whereas the state input correlithm object 1002 and thecorresponding correlithm objects in row 1010 of truth table 1008 are64-bit digital words. As a result, the device 1000 allocates more weightto the logical value of the set/reset input correlithm object 1004 thanthe logical value of state input correlithm object 1002 when determiningthe appropriate state of device 1000, as described in detail below.

The operation of binary correlithm object flip-flop device 1000 isdescribed below in conjunction with a particular, non-limiting example.At the outset of the example operation, the state of the device 1000 asrepresented by state input correlithm object 1002 is non-determinative:it can be a logical “0” or a logical “1.” For purposes of this example,however, assume it is a logical “1.” Further assume that the set/resetinput correlithm object 1004 represents a logical “0.” Device 1000receives both the state input correlithm object 1002 and the set/resetinput correlithm object 1004.

Device 1000 determines the distance in n-dimensional space between thestate input correlithm object 1002 (representing logical “1” in thisexample) and the correlithm object representing a logical “0” in row1010 of the truth table 1008. In a particular embodiment, this distancein n-dimensional space is represented by determining a Hamming distance1020 between the binary values of state input correlithm object 1002 andthe binary values of the correlithm object representing a logical “0” inrow 1010 of the truth table 1008. Device 1000 then determines a Hammingdistance 1022 between state input correlithm object 1002 (representinglogical “1” in this example) and the correlithm object representing alogical “1” in row 1010 of the truth table 1008.

If the state of the device 1000 as represented by state input correlithmobject 1002 is a logical “0,” then the Hamming distance 1020 between itand the correlithm object representing a logical “0” in row 1010 of thetruth table 1008 will be very small, trending to zero depending on howmuch noise has altered the binary values of either the state inputcorrelithm object 1002 and/or the correlithm object representing alogical “0” in row 1010 of the truth table 1008. Conversely, the Hammingdistance 1022 between it and the correlithm object representing alogical “1” in row 1010 of the truth table 1008 will be large, trendingto “n” for an n-bit correlithm object 1002 (e.g., Hamming distance 1022trends to 64 for a 64-bit correlithm object 1002). If the state of thedevice 1000 as represented by state input correlithm object 1002 is alogical “1,” (as it is in this particular example) then the Hammingdistance calculations 1020 and 1022 will be reversed. In particular, theHamming distance 1020 between state input correlithm object 1002(representing logical “1” in this example) and the correlithm objectrepresenting a logical “0” in row 1010 of the truth table 1008 willtrend toward “n” for an n-bit correlithm object 1002; and the Hammingdistance 1022 between state input correlithm object 1002 (representinglogical “1” in this example) and the correlithm object representing alogical “1” in row 1010 of the truth table 1008 will trend toward zerodepending on the amount of noise, as described above.

Device 1000 also determines the Hamming distance 1024 between theset/reset input correlithm object 1004 and the correlithm objectrepresenting a logical “0” in row 1012 of the truth table 1008; and theHamming distance 1026 between set/reset input correlithm object 1004 andthe correlithm object representing a logical “1” in row 1012 of thetruth table 1008. If set/reset input correlithm object 1004 represents alogical “0,” (as it is in this particular example), then the Hammingdistance 1024 between it and the correlithm object representing alogical “0” in row 1012 of the truth table 1008 will be very small,trending to zero depending on how much noise has altered the binaryvalues of either the set/reset input correlithm object 1004 and/or thecorrelithm object representing a logical “0” in row 1012 of the truthtable 1008. Conversely, the Hamming distance 1026 between it and thecorrelithm object representing a logical “1” in row 1012 of the truthtable 1008 will be large, trending to “n” for an n-bit correlithm object1002 (e.g., Hamming distance 1026 will be trending to 128 for a 128-bitcorrelithm object 1004).

The Hamming distances 1020 and 1024 between the correlithm objects 1002and 1004 and their corresponding correlithm objects representing logical“0's” in truth table 1008, respectively, are added together; and theHamming distances 1022 and 1026 between the correlithm objects 1002 and1004 and their corresponding correlithm objects representing logical“1's” in truth table 1008, respectively, are also added together. Theoutput correlithm object 1006 is set to represent a logical “0” if theaddition of the Hamming distances 1020 and 1024 for the logical “0”values is less than the addition of the Hamming distances 1022 and 1026for the logical “1” values. Conversely, the output correlithm object1006 is set to represent a logical “1” if the addition of the Hammingdistances 1022 and 1026 for the logical “1” values is less than theaddition of the Hamming distances 1020 and 1024 for the logical “0”values.

In the example above where the state input correlithm object 1002 was alogical “1” and the set/reset input correlithm object was a logical “0,”the Hamming distance 1020 trended to 64, the Hamming distance 1022trended to zero, the Hamming distance 1024 trended to zero, and theHamming distance 1026 trended to 128. Thus, the addition of Hammingdistances 1020 and 1024 (e.g., 64+0=64) was less than the addition ofHamming distances 1022 and 1026 (e.g., 0+128=128). Therefore, outputcorrelithm object 1006 is set to a logical “0” value. In this way, thestate of the device 1000 was flipped from a logical “1” value to alogical “0” value.

Because the set/reset input correlithm object 1004 is a larger n-bitdigital word than state input correlithm object 1002 (e.g., 128-bitversus 64-bit), device 1000 effectively gives more weight to the Hammingdistances 1024 and 1026 for reset input correlithm object 1004 than theHamming distances 1020 and 1022 for the state input correlithm object1002. For example, the Hamming distance 1026 between the set/reset inputcorrelithm object 1004 (representing logical “0” in this example) andthe correlithm object representing logical “1” in row 1012 of truthtable 1008 is 128, whereas the Hamming distance 1020 between the stateinput correlithm object 1002 (representing logical “1” in this example)and the correlithm object representing logical “0” in row 1010 of truthtable 1008 is 64. By applying more weight to the set/reset inputcorrelithm object 1004 than state input correlithm object 1002 indetermining the logical value of output correlithm object 1006, device1000 is permitted to change states.

Continuing with the example, the correlithm object output 1006representing a logical “0” is fed back to device 1000 as state inputcorrelithm object 1002. In the continuation of this example, set/resetinput correlithm object 1004 still represents a logical “0” value. Withthe state input correlithm object 1002 now representing a logical “0”instead of the previous logical “1,” the Hamming distance 1020 trends tozero and the Hamming distance 1022 trends to 64. The Hamming distance1024 remains trending to zero and the Hamming distance 1026 remainstrending to 128. The addition of Hamming distances 1020 and 1024 (e.g.,0+0=0) is less than the addition of Hamming distances 1022 and 1026(e.g., 64+128=192). Thus, the correlithm object output 1006 remains alogical “0” value. In effect, the state of device 1000 has latched to alogical “0” value.

From here, if the set/reset input correlithm object 1004 is changed to alogical “1” value, then the state of the device 1000 will change to alogical “1” value. In particular, with the state input correlithm object1002 still representing a logical “0,” the Hamming distance 1020 trendsto zero and the Hamming distance 1022 trends to 64. However, the Hammingdistance 1024 now trends to 128 and the Hamming distance 1026 now trendsto zero. Thus, the addition of Hamming distances 1022 and 1026 (e.g.,64+0=64) is less than the addition of Hamming distances 1020 and 1024(e.g., 0+128=128). Thus, the output correlithm object 1006 changes to alogical “1” value.

Once again, the correlithm object output 1006 representing a logical “1”is fed back to device 1000 as state input correlithm object 1002. In thecontinuation of this example, set/reset input correlithm object 1004still represents a logical “1” value. With the state input correlithmobject 1002 now representing a logical “1” instead of the previouslogical “0,” the Hamming distance 1020 trends to 64 and the Hammingdistance 1022 trends to zero. The Hamming distance 1024 remains trendingto 128 and the Hamming distance 1026 remains trending to zero. Theaddition of Hamming distances 1022 and 1026 (e.g., 0+0=0) is less thanthe addition of Hamming distances 1020 and 1024 (e.g., 64+128=192).Thus, the correlithm object output 1006 remains a logical “1” value. Ineffect, the state of device 1000 has latched to a logical “1” value.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A device configured to emulate a correlithm object logic functiongate, comprising: a memory operable to store a logical operator truthtable, the truth table comprising: a plurality of input logical valuesrepresented by a first plurality of correlithm objects; a plurality ofoutput logical values represented by a second plurality of correlithmobjects; a plurality of logical operators represented by a thirdplurality of correlithm objects; a logic engine communicatively coupledto the memory and configured to: receive at least one input representinga logical value; receive a context input correlithm object representingone of the plurality of logical operators; determine a portion of thetruth table to apply based at least in part upon the logical operatorrepresented by the context input correlithm object; and determine anoutput of the logic function gate based at least in part upon thedetermined portion of the truth table to apply and the input.
 2. Thedevice of claim 1, wherein the correlithm object logic function gate isa unary correlithm object logic function gate and the plurality of inputlogical values represented by the first plurality of correlithm objectscomprises: a first logical state value represented by a first correlithmobject; and a second logical state value represented by a secondcorrelithm object.
 3. The device of claim 2, wherein the first logicalstate value comprises a logical zero value and the second logical statevalue comprises a logical one.
 4. The device of claim 2, wherein theplurality of output logical values represented by the second pluralityof correlithm objects comprises: the first logical state valuerepresented by a third correlithm object; and the second logical statevalue represented by a fourth correlithm object.
 5. The device of claim4, wherein the input is a logical value represented by a fifthcorrelithm object and the logic engine is further operable to: determinea Hamming distance between the fifth correlithm object and the firstcorrelithm object; determine a Hamming distance between the fifthcorrelithm object and the second correlithm object; select one of thefirst correlithm object and the second correlithm object based on whichhas the smaller Hamming distance to the fifth correlithm object; anddetermine the output based at least in part upon the selection of thefirst correlithm object and the second correlithm object.
 6. The deviceof claim 1, wherein the correlithm object logic function gate is abinary correlithm object logic function gate and the plurality of inputlogical values are a first plurality of input logical values comprising:a first logical state value represented by a first correlithm object;and a second logical state value represented by a second correlithmobject; and the truth table further comprising: a second plurality ofinput logical values comprising: the first logical state valuerepresented by a third correlithm object; and the second logical statevalue represented by a fourth correlithm object.
 7. The device of claim6, wherein the first logical state value comprises a logical zero valueand the second logical state value comprises a logical one.
 8. Thedevice of claim 2, wherein the plurality of output logical valuesrepresented by the second plurality of correlithm objects comprises: thefirst logical state value represented by a fifth correlithm object; andthe second logical state value represented by a sixth correlithm object.9. The device of claim 8, wherein the input is a first input representedby a seventh correlithm object, and the logic engine is further operableto: receive a second input that is a logical value represented by aneighth correlithm object; determine a Hamming distance between theseventh correlithm object and the first correlithm object; determine aHamming distance between the seventh correlithm object and the secondcorrelithm object; select one of the first correlithm object and thesecond correlithm object based on which has the smaller Hamming distanceto the fifth correlithm object; determine a Hamming distance between theeighth correlithm object and the third correlithm object; determine aHamming distance between the eighth correlithm object and the fourthcorrelithm object; select one of the third correlithm object and thefourth correlithm object based on which one has the smaller Hammingdistance to the eighth correlithm object; and determine the output basedat least in part upon the selection of the first and second correlithmobjects and the selection of the third and fourth correlithm objects.10. The device of claim 1, wherein each of the correlithm objectscomprises an n-bit digital word comprising binary values.
 11. The deviceof claim 1, wherein determining a portion of the truth table to applycomprises: determining a Hamming distance between the context inputcorrelithm object and each of the third plurality of correlithm objectsrepresenting the logical operators; and selecting the logical operatorbased on which one is represented by a correlithm object having thesmallest Hamming distance to the context input correlithm object. 12.The device of claim 11, wherein the portion of the truth table to applycomprises a subset of the plurality of output logical values that isdetermined by applying a logic function associated with the selectedlogical operator to the plurality of input logical values.
 13. Thedevice of claim 1, wherein the output of the logic function gate isrepresented by a correlithm object.
 14. A method for emulating acorrelithm object logic function gate, comprising: storing a logicaloperator truth table, the truth table comprising: a plurality of inputlogical values represented by a first plurality of correlithm objects; aplurality of output logical values represented by a second plurality ofcorrelithm objects; a plurality of logical operators represented by athird plurality of correlithm objects; receiving at least one inputrepresenting a logical value; receiving a context input correlithmobject representing one of the plurality of logical operators;determining a portion of the truth table to apply based at least in partupon the logical operator represented by the context input correlithmobject; and determining an output of the logic function gate based atleast in part upon the determined portion of the truth table to applyand the input.
 15. The method of claim 14, wherein each of thecorrelithm objects comprises an n-bit digital word of binary values. 16.The method of claim 14, wherein determining a portion of the truth tableto apply comprises: determining a Hamming distance between the contextinput correlithm object and each of the third plurality of correlithmobjects representing the logical operators; and selecting the logicaloperator based on which one is represented by a correlithm object havingthe smallest Hamming distance to the context input correlithm object.17. The method of claim 16, wherein the portion of the truth table toapply comprises a subset of the plurality of output logical values thatis determined by applying a logic function associated with the selectedlogical operator to the plurality of input logical values.
 18. Themethod of claim 14, wherein the output of the logic function gate isrepresented by a correlithm object.
 19. A device configured to emulate acorrelithm object logic function gate, comprising: a memory operable tostore a logical operator truth table, the truth table comprising: aplurality of input logical values represented by a first plurality ofcorrelithm objects; a plurality of output logical values represented bya second plurality of correlithm objects; a plurality of logicaloperators represented by a third plurality of correlithm objects; alogic engine communicatively coupled to the memory and configured to:receive at least one input representing a logical value; receive acontext input correlithm object representing one of the plurality oflogical operators; determine a portion of the truth table to apply basedat least in part upon the logical operator represented by the contextinput correlithm object; and determine an output of the logic functiongate based at least in part upon the determined portion of the truthtable to apply and the input; wherein determining a portion of the truthtable to apply comprises: determining a Hamming distance between thecontext input correlithm object and each of the third plurality ofcorrelithm objects representing the logical operators; and selecting thelogical operator based on which one is represented by a correlithmobject having the smallest Hamming distance to the context inputcorrelithm object; and wherein the portion of the truth table to applycomprises a subset of the plurality of output logical values that isdetermined by applying a logic function associated with the selectedlogical operator to the plurality of input logical values.
 20. Thedevice of claim 19, wherein each of the correlithm objects comprises ann-bit digital word of binary values.